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| Pre-Trained Large Model Empowering Scenario Planning and Bi-Level Nested Optimization for Optimal Scheduling of Multi-Energy Complementary System |
| Wang Kaiyan1, Zhu Hengtao1, Jia Rong1, Ming Bo2, Dang Jian1 |
1. School of Electrical Engineering Xi'an University of Technology Xi'an 710048 China;
2. State Key Laboratory of Water Engineering Ecology and Environment in Arid Area Xi'an University of Technology Xi'an 710048 China |
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Abstract Renewable energy source (RES) power generation has strong stochastic volatility, and large-scale grid-connection will have an impact on the consumption of RES and scheduling operation planning of the power system, which is not conducive to stable economic operation. Constructing multi-energy complementary systems represents a viable solution to enhance energy utilization and mitigate dispatch operational risks. Scholars at home and abroad have conducted extensive research on the problem of multi-energy complementarity, and the following problems still exist at this stage: (1) In the regional power grid with large RES integration, thermal power and conventional hydropower units will frequently start-stop, climb over the limit, adjust the overdraft. Meanwhile, technical difficulties such as the high cost and large capacity integration of chemical energy storage have not been broken through. (2) The performance of existing power forecasting models needs to be further improved, and accurate prediction of wind, solar and load power is the premise of efficient utilization of RES. To address these issues, this paper proposed a pre-trained large language model (PLLM) based on empowering scenario planning and bi-level nested optimization of wind-solar-hydro-thermal-pumped storage complementary scheduling model.
Firstly, after cleaning and normalizing the data, fixed step time series input tokens are constructed to enhance the model's capability to capture local data features and reduce the redundancy of input information. The prompt word pattern framework is innovatively designed to integrate the context feature information with the input tokens to guide the PLLM reasoning process. In order to improve the wind-solar-load integrated power prediction performance of PLLM, an efficient fine-tuning strategy is introduced to adjust the relevant weight parameters. Subsequently, The K-means clustering algorithm integrated with PLLM is constructed to generate supply-demand coupling operation scenarios conforming to the power system scheduling production simulation according to the integrated power prediction results, which provides scenarios support for the next optimal scheduling model. Finally, a bi-level nested optimization method is proposed for the coordinated operation of each power source in wind-solar-hydro-thermal-pumped storage complementary system. The upper level optimizes the stability of the output power of the system to guarantee the consumption of RES, while the lower level optimizes the low-carbon economic scheduling of the system and the thermal power unit commitments to realize the economy and environmental protection.
According to the test results of the actual data sets, compared with the traditional neural network prediction models gate recurrent unit (GRU), long-short term memory (LSTM), temporal convolutional network (TCN) and bidirectional long-short term memory (BiLSTM) integrated network TCN-BiLSTM. The evaluation indexes of the model's prediction performance, mean absolute error (MAE), root mean absolute error (RMSE) and mean absolute percentage error (MAPE) are decreased by an average of 46.45%, 42.08% and 23.25%, respectively, and R2 increased by an average of 7.26%. The superiority and adaptability of the proposed PLLM in power prediction are demonstrated. The simulation results of a base project in a northwestern province demonstrates that the proposed bi-level nested solution method effectively coordinates the rapid regulation capabilities of conventional hydropower and pumped storage to respond to real-time fluctuations in wind-solar-load integrated power output, and realize the economy and low-carbon of the system operation on the basis of ensuring the stability of the output power. After pumped storage is involved in the complementary operation, the average values of the source load fluctuation smoothing factor, system operating cost and CO2 emissions are reduced by 13.92%, 1.04% and 1.17%, respectively, the average values of total number of thermal power units, start-stop times and start-stop costs are reduced by 2 times, 4 times and 2 800 yuan, respectively. When pumped storage participates in the peaking operation of the coupled system, it can significantly improve the flexible adjustment margin, alleviate the peaking pressure of thermal power units, and greatly reduce the operating cost and CO2 emissions of the system.
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Received: 06 March 2025
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2026, Vol. 41 
